Machine Learning Approaches for Automated Mental Disorder Classification based on Social Media Textual Data

The application of machine learning models to mental health-related text data offers a novel approach to discern patterns and trends, aiding in the identification of subgroups and personalized treatment options. This research explores the classification of mental disorders based on text data extracted from subreddits focused on mental health. The dataset consists of 10,000 rows of text collected from four subreddits: 'BPD', 'bipolar', 'depression', and 'Anxiety', along with a combined category 'others' encompassing 'mentalillness' and 'schizophrenia'. To enable the application of machine learning models, various text preprocessing techniques were applied, including the removal of URLs, punctuation marks, and stopwords, as well as the transformation of raw text documents into a matrix of TF-IDF features. These preprocessing steps were performed on both the titles and text contents of the posts. Three machine learning models, namely Multinomial Naive Bayes, Multi-layer Perceptron, and LightGBM, were employed for the classification task. The models were trained and evaluated separately on both the post titles and the text content. The accuracy of each model was assessed to measure their performance. The results indicate that the Multinomial Naive Bayes model achieved an accuracy of 0.706 when classifying based on titles, while the accuracy increased to 0.73 when classifying based on the text content. The Multi-layer Perceptron model yielded an accuracy of 0.68 for title classification and 0.714 for text content classification. Notably, the LightGBM model exhibited superior performance, achieving an accuracy of 0.724 when using titles for classification, and an even higher accuracy of 0.77 when employing the text content. This research demonstrates the efficacy of machine learning models in classifying mental disorders using text data extracted from social media. These findings contribute to the ongoing exploration of using social media data for mental health analysis and may aid in developing automated tools for early detection and support for individuals facing mental health challenges

What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201

Mental distress detection and triage in forum posts: the LT3 CLPsych 2016 shared task system

This paper describes the contribution of LT3 for the CLPsych 2016 Shared Task on automatic triage of mental health forum posts. Our systems use multiclass Support Vector Machines (SVM), cascaded binary SVMs and ensembles with a rich feature set. The best systems obtain macro-averaged F-scores of 40% on the full task and 80% on the green versus alarming distinction. Multiclass SVMs with all features score best in terms of F-score, whereas feature filtering with bi-normal separation and classifier ensembling are found to improve recall of alarming posts

Depression and Self-Harm Risk Assessment in Online Forums

Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.Comment: Expanded version of EMNLP17 paper. Added sections 6.1, 6.2, 6.4, FastText baseline, and CNN-